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checkout: add --jobs support
Use multiprocessing to run in parallel. When operating on multiple projects, this can speed things up. Across 1000 repos, it goes from ~9sec to ~5sec with the default -j8. Change-Id: Ida6dd565db78ff7bac0ecb25d2805e8a1bf78048 Reviewed-on: https://gerrit-review.googlesource.com/c/git-repo/+/297982 Reviewed-by: Chris Mcdonald <cjmcdonald@google.com> Tested-by: Mike Frysinger <vapier@google.com>
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@ -12,8 +12,11 @@
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# See the License for the specific language governing permissions and
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# limitations under the License.
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import functools
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import multiprocessing
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import sys
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import sys
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from command import Command
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from command import Command, DEFAULT_LOCAL_JOBS, WORKER_BATCH_SIZE
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from progress import Progress
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from progress import Progress
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@ -31,27 +34,41 @@ The command is equivalent to:
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repo forall [<project>...] -c git checkout <branchname>
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repo forall [<project>...] -c git checkout <branchname>
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"""
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"""
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PARALLEL_JOBS = DEFAULT_LOCAL_JOBS
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def ValidateOptions(self, opt, args):
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def ValidateOptions(self, opt, args):
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if not args:
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if not args:
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self.Usage()
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self.Usage()
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def _ExecuteOne(self, nb, project):
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"""Checkout one project."""
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return (project.CheckoutBranch(nb), project)
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def Execute(self, opt, args):
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def Execute(self, opt, args):
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nb = args[0]
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nb = args[0]
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err = []
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err = []
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success = []
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success = []
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all_projects = self.GetProjects(args[1:])
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all_projects = self.GetProjects(args[1:])
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pm = Progress('Checkout %s' % nb, len(all_projects))
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def _ProcessResults(results):
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for project in all_projects:
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for status, project in results:
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pm.update()
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status = project.CheckoutBranch(nb)
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if status is not None:
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if status is not None:
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if status:
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if status:
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success.append(project)
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success.append(project)
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else:
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else:
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err.append(project)
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err.append(project)
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pm.update()
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pm = Progress('Checkout %s' % nb, len(all_projects))
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# NB: Multiprocessing is heavy, so don't spin it up for one job.
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if len(all_projects) == 1 or opt.jobs == 1:
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_ProcessResults(self._ExecuteOne(nb, x) for x in all_projects)
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else:
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with multiprocessing.Pool(opt.jobs) as pool:
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results = pool.imap_unordered(
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functools.partial(self._ExecuteOne, nb), all_projects,
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chunksize=WORKER_BATCH_SIZE)
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_ProcessResults(results)
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pm.end()
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pm.end()
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if err:
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if err:
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